Acoustic tomography (AT) is one of a few non-contact measurement techniques that can present information about the temperature distribution. Its successful application greatly depends on the performance of the reconstruction algorithm. In this paper, a temperature distribution reconstruction method based on compressed sensing (CS) is proposed. Firstly, a measurement matrix of an AT system in a CS framework is established. Secondly, a sparse basis is selected based on the mutual coherence between the measurement matrix and sparse basis. Thirdly, an improvement of the orthogonal matching pursuit (OMP) algorithm, called the IMOMP algorithm, is proposed for pursuing efficiency in recovering sparse signals. Reconstruction experiments of Gaussian sparse signals showed that IMOMP was better than OMP in both success ratio and running time, and the selection method of sparse basis was effective. Finally, a temperature distribution reconstruction algorithm based on compressed sensing, that is, the CS-IMOMP algorithm, is proposed. Simulation and experiment results show that, compared with the least square algorithm and the Simultaneous Iterative Reconstruction Technique algorithm, the CS-IMOMP algorithm has smaller reconstruction errors and provides more accurate information about the temperature distribution.

Aiming at that bounding box algorithm's coverage is depend on the anchor nodes density in network and DV-Hop has a larger location error, this paper presents a hybrid algorithm that combines the bounding box localization algorithm and DV-Hop localization algorithm, which is called BDV-Hop algorithm. S imulating in MATLAB platform, we analyze bounding box, DV-Hop and the BDV-Hop algorithm respectively. Extensive simulation show that under a variety of network conditions, BDV-Hop algorithm inherits both the advantage of bounding box that has smaller location error and cost, and the advantage of D V-Hop that has high location coverage. At the same time BDV-Hop algorithm have a better adaptability.

Abstract. This paper deals with the optimization of kurtosis for complex-valued signals in the independent component analysis (ICA) framework, where source signals are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast schemes, a similar contrast function is put forward, based on which a new fast fixed-point (FastICA) algorithm is proposed. The new optimization method is similar in spirit to the former classical kurtosis-based FastICA algorithm but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. The performance of this new algorithm is confirmed through computer simulations.

Cross-correlation is a common time delay estimation method. However, in the actual measurement environment, the near-end and far-end sensors are often disturbed by the correlated noise, which seriously affects the accuracy of time delay estimation and even leads algorithm is proposed, and the steepest descent is introduced to reduce the sensitivity of FastICA algorithm to initial values. The useful acoustic data and disturbing acoustic data were measured respectively in a simulated granary. From these data, the signals contaminated by noise were generated by a mixing matrix and used for testing the performance of the proposed method. The testing results show that the proposed method can effectively suppress the influence of correlated noise on cross-correlation time delay estimation, and improve the accuracy of estimation.

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